Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks (original) (raw)
Related papers
2020
With the growth of the population concentrated in urban areas of large agglomerations, the need for e cient and resilient multi-modal transportation systems is paramount. To model, analyze and improve transportation dynamics at large scale, complex networks represent an extremely versatile toolkit: multi-modal mobility networks can be modelled as a multi-layered weighted graph. In the last decade, several works [1, 2, 3] have shown that complex network approaches based on computation of centrality metrics can be extremely useful to model and analyze the resilience properties of complex networks. In such representation, each layer of the graph can be associated to a transportation mode (e.g., road, metro, buses, etc); each node of the network is an intersection between roads, a parking spot or a bus/metro stop/station; and the edges are links between the nodes, possibly belonging to di↵erent layers of the transportation network (e.g., links connecting bus with metro stations or parki...
Graph Theory Approach to the Vulnerability of Transportation Networks
Algorithms
Nowadays, transport is the basis for the functioning of national, continental, and global economies. Thus, many governments recognize it as a critical element in ensuring the daily existence of societies in their countries. Those responsible for the proper operation of the transport sector must have the right tools to model, analyze, and optimize its elements. One of the most critical problems is the need to prevent bottlenecks in transport networks. Thus, the main aim of the article was to define the parameters characterizing the transportation network vulnerability and select algorithms to support their search. The parameters proposed are based on characteristics related to domination in graph theory. The domination, edge-domination concepts, and related topics, such as bondage-connected and weighted bondage-connected numbers, were applied as the tools for searching and identifying the bottlenecks in transportation networks. Furthermore, the algorithms for finding the minimal domi...
Network planning and traffic flow optimization requires the acquirement and analysis of large quantities of data such as the network topology, its traffic flow data, vehicle fleet composition, emission measurements etc. Data acquirement is an expensive process that involves household surveys and automatic as well as semi-automatic measurements performed all over the network. For example, in order to accurately estimate the effect of a certain network change on the total emissions produced by vehicles in the network, assessment of the vehicle fleet composition for each origin-destination pair is required. As a result, problems that optimize non-local merit functions becomes highly difficult to solve. One such problem is finding the optimal deployment of traffic monitoring units. In this paper we suggest a new traffic assignment model that is based on the concept of Shortest Path Betweenness Centrality measure borrowed from the domain of complex network analysis. We show how Betweenness can be augmented in order to solve the traffic assignment problem given an arbitrary travel cost definition. The proposed traffic assignment model is evaluated using a high resolution Israeli transportation dataset derived from the analysis of cellular phones data. The group variant of the augmented Betweenness Centrality is then used to optimize the locations of traffic monitoring units, hence reducing the cost and increasing the effectiveness of traffic monitoring.
2007
The urban road network provides spatial access to the city through an overlapping hierarchy, ranging from highways to local access streets. This pattern of network organisation has led to an increased propensity to vulnerability, exposing parts of the city to sharp decreases in accessibility when traffic blockages occur on the main links or at junctions. Our aim is to define road network vulnerability and to measure the road network's exposure to risk. We postulate that the network morphology, structure and level of congestion can be influencing factors. Two vulnerability indices which pinpoint accessibility loss in the city by removing links and vertices one by one, have been developed to assess the network's vulnerability.